Results of Evolution Supervised by Genetic Algorithms

A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.Comment: 6 pages, 1 Table, 2 figure

The effects of reconditioning by welding of crankshafts in automotive industry

The reconditioning by welding process applied to the crankshafts in the automotive industry can be carried out by using various reconditioning technologies that are based on different welding parameters and processes. This paper presents a comparison between Shielded Metal Arc Welding (SMAW) and Welding in Gas (WIG) reconditioning processes from the perspective of the metallographic analysis conducted on the zones resulted after the depositing process. The heat cycle resulted during the two welding processes influences in a different manner the welding behavior of the base material due to the occurrence of micro-structural changes in the main zones of the deposit. The occurred structural changes may influence to a significant degree the operating behavior of the structures repaired by welding

Clothes size prediction from dressed-human silhouettes

© 2017, Springer International Publishing AG. We propose an effective and efficient way to automatically predict clothes size for users to buy clothes online. We take human height and dressed-human silhouettes in front and side views as input, and estimate 3D body sizes with a data-driven method. We adopt 20 body sizes which are closely related to clothes size, and use such 3D body sizes to get clothes size by searching corresponding size chart. Previous image-based methods need to calibrate camera to estimate 3D information from 2D images, because the same person has different appearances of silhouettes (e.g. size and shape) when the camera configuration (intrinsic and extrinsic parameters) is different. Our method avoids camera calibration, which is much more convenient. We set up our virtual camera and train the relationship between human height and silhouette size under this camera configuration. After estimating silhouette size, we regress the positions of 2D body landmarks. We define 2D body sizes as the distances between corresponding 2D body landmarks. Finally, we learn the relationship between 2D body sizes and 3D body sizes. The training samples for each regression process come from a database of 3D naked and dressed bodies created by previous work. We evaluate the whole procedure and each process of our framework. We also compare the performance with several regression models. The total time-consumption for clothes size prediction is less than 0.1, s and the average estimation error of body sizes is 0.824, cm, which can satisfy the tolerance for customers to shop clothes online

SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing

While models of 3D clothing learned from real data exist, no method can predict clothing deformation as a function of garment size. In this paper, we introduce SizerNet to predict 3D clothing conditioned on human body shape and garment size parameters, and ParserNet to infer garment meshes and shape under clothing with personal details in a single pass from an input mesh. SizerNet allows to estimate and visualize the dressing effect of a garment in various sizes, and ParserNet allows to edit clothing of an input mesh directly, removing the need for scan segmentation, which is a challenging problem in itself. To learn these models, we introduce the SIZER dataset of clothing size variation which includes $100$ different subjects wearing casual clothing items in various sizes, totaling to approximately 2000 scans. This dataset includes the scans, registrations to the SMPL model, scans segmented in clothing parts, garment category and size labels. Our experiments show better parsing accuracy and size prediction than baseline methods trained on SIZER. The code, model and dataset will be released for research purposes.Comment: European Conference on Computer Vision 202

General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues

Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation - skeleton, volumetric shape, appearance, and optionally a body surface - and estimates the actor's motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume raycasting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, as well as variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way